Edge computing is a new paradigm enabling intelligent applications for the Internet of Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the resource limitations of Internet of Things devices, it is essential to use these resources optimally. Therefore, intelligence needs to be applied through an efficient deep learning model to optimize resources like memory, power, and computational ability. In addition, intelligent edge computing is essential for real-time applications requiring end-to-end delay or response time within a few seconds. We propose decentralized heterogeneous edge clusters deployed with an optimized pre-trained yolov2 model. In our model, the weights have been pruned and then split into fused layers and distributed to edge devices for processing. Later the gateway device merges the partial results from each edge device to obtain the processed output. We deploy a convolutional neural network (CNN) on resource-constraint IoT devices to make them intelligent and realistic. Evaluation was done by deploying the proposed model on five IoT edge devices and a gateway device enabled with hardware accelerator. The evaluation of our proposed model shows significant improvement in terms of communication size and inference latency. Compared to DeepThings for 5 X 5 fused layer partitioning for five devices, our proposed model reduces communication size by ∼ 14.4% and inference latency by ∼16%.

No abstract

No abstract

No abstract

Although Machine Learning (ML) has become synonymous for Artificial Intelligence (AI); recently, Deep Learning (DL) is being used in place of machine learning persistently. If statistics is grammar and machine learning is poetry then deep learning is the creation of Socrates. While machine learning is busy in supervised and unsupervised methods, deep learning continues its motivation for replicating the human nervous system by incorporating advanced types of Neural Networks (NN). Due to its practicability, deep learning is finding its applications in various AI solutions such as computer vision, natural language processing, intelligent video analytics, analyzing hyperspectral imagery from satellites and so on. Here we have made an attempt to demonstrate strong learning ability and better usage of the dataset for feature extraction by deep learning. This paper provides an introductory tutorial to the domain of deep learning with its history, evolution, and introduction to some of the sophisticated neural networks such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This work will serve as an introduction to the amazing field of deep learning and its potential use in dealing with today’s large chunk of unstructured data, that it could take decades for humans to comprehend and extract relevant information.